Edge computing has matured from buzzword to essential architecture pattern. With users expecting sub-100ms response times and regulations increasingly requiring data locality, moving computation to the edge is no longer optional for many applications. Here are the patterns that are proving their worth in production.
Pattern 1: CDN Compute
The lowest barrier to edge computing is running logic at your CDN's edge nodes. Cloudflare Workers, Vercel Edge Functions, and Deno Deploy offer sub-millisecond cold starts with global distribution. Use this pattern for: A/B testing, authentication token validation, request routing, personalization, and API response transformation. These platforms now support WebAssembly, databases (D1, Turso), and key-value stores at the edge.
Pattern 2: Regional Inference
For ML-powered features, running inference at regional edge locations (rather than centralized GPU clusters) dramatically reduces latency. Small models (under 2B parameters) run efficiently on CPU-optimized instances at edge locations. The architecture: centralized training and model management, with optimized inference models distributed to 15-30 regional points of presence.
Pattern 3: Hybrid Edge-Cloud
Most real-world architectures are hybrid. The edge handles latency-sensitive operations (authentication, caching, light compute), while the cloud handles heavy lifting (complex queries, batch processing, model training). The key is designing clear boundaries and async sync mechanisms between the two tiers.
